On the robustness of Bayesian networks to learning from non-conjugate sampling

DOI10.1016/j.ijar.2010.01.013zbMath1205.68317OpenAlexW2086855087MaRDI QIDQ985148

J. Martínez

Publication date: 20 July 2010

Published in: International Journal of Approximate Reasoning (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.ijar.2010.01.013

zbMATH Keywords

Bayesian networksBayesian robustnesstotal variation distanceisoseparation propertylocal derobertis distance

Mathematics Subject Classification ID

Learning and adaptive systems in artificial intelligence (68T05) Theory of languages and software systems (knowledge-based systems, expert systems, etc.) for artificial intelligence (68T35)

Related Items (2)

Measuring local sensitivity in Bayesian inference using a new class of metrics ⋮ Implicit inequality constraints in a binary tree model

Cites Work

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• Parameter priors for directed acyclic graphical models and the characterization of several probability distributions
• Extreme inaccuracies in Gaussian Bayesian networks
• Importance sampling for Bayesian sensitivity analysis
• Numerical treatment of Bayesian robustness problems
• Bayesian inference using intervals of measures
• Invariance properties of density ratio priors
• Stable decision problems
• Bayesian networks for discrete multivariate data: An algebraic approach to inference
• Local sensitivity diagnostics for Bayesian inference
• Sequential updating of conditional probabilities on directed graphical structures
• Bayesian robustness modeling using regularly varying distributions

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